{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "87e3ba6e-e23a-424c-9395-9ba1147d35cf",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Joinership Data Analysis\n",
    "\n",
    "## By Ragini Srinivasan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f7df38ee-c9a1-466f-89e2-0cfe8f9615ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.graphics.regressionplots import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "df_total = pd.read_csv(\"~/Downloads/final-lebensraum-data.csv\")\n",
    "df_over_100k = df_total.query('over_100000 == True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "86841ab6-afa2-422e-a999-b79253189d67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total Joiners per 1000 vs. TB Mortality Increase 1914-1918"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "704b64bb-37c8-49a3-ad2d-463f90affae6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TB MORTALITY AND CITIES OVER 100K:\n",
      "                              OLS Regression Results                              \n",
      "==================================================================================\n",
      "Dep. Variable:     total_joiners_per_1000   R-squared:                       0.108\n",
      "Model:                                OLS   Adj. R-squared:                  0.074\n",
      "Method:                     Least Squares   F-statistic:                     3.159\n",
      "Date:                    Fri, 14 Oct 2022   Prob (F-statistic):             0.0872\n",
      "Time:                            11:22:36   Log-Likelihood:                 2.6174\n",
      "No. Observations:                      28   AIC:                            -1.235\n",
      "Df Residuals:                          26   BIC:                             1.430\n",
      "Df Model:                               1                                         \n",
      "Covariance Type:                nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept         0.9288      0.243      3.819      0.001       0.429       1.429\n",
      "ratio_mort_tb    -0.2648      0.149     -1.777      0.087      -0.571       0.041\n",
      "==============================================================================\n",
      "Omnibus:                        2.245   Durbin-Watson:                   2.065\n",
      "Prob(Omnibus):                  0.325   Jarque-Bera (JB):                0.984\n",
      "Skew:                           0.277   Prob(JB):                        0.611\n",
      "Kurtosis:                       3.733   Cond. No.                         12.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cities Over 100k\n",
    "\n",
    "# Table 6.5\n",
    "joinership_tb_mortality_1 = sm.formula.ols(\"total_joiners_per_1000 ~ ratio_mort_tb\", data = df_over_100k).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TB MORTALITY AND CITIES OVER 100K:\")\n",
    "print(joinership_tb_mortality_1.summary())\n",
    "print(\"\\n\")\n",
    "\n",
    "# Figure 6.9\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_over_100k.ratio_mort_tb, df_over_100k.total_joiners_per_1000)\n",
    "plt.xlabel(\"TB Mortality Change from 1914 to 1918\")\n",
    "plt.ylabel(\"Total Number of Joiners per 1000\")\n",
    "plt.title(\"Joinership vs. TB Mortality Increase\")\n",
    "plt.plot([1.0, 2.2], [joinership_tb_mortality_1.params[0] + joinership_tb_mortality_1.params[1] * 1.0,\n",
    "                      joinership_tb_mortality_1.params[0] + joinership_tb_mortality_1.params[1] * 2.2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9393ece1-8472-460a-b471-f86490fc653f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TB MORTALITY AND CITIES OVER 100K + SMALLER CITIES + GROUPED CITIES:\n",
      "                              OLS Regression Results                              \n",
      "==================================================================================\n",
      "Dep. Variable:     total_joiners_per_1000   R-squared:                       0.030\n",
      "Model:                                OLS   Adj. R-squared:                  0.006\n",
      "Method:                     Least Squares   F-statistic:                     1.260\n",
      "Date:                    Fri, 14 Oct 2022   Prob (F-statistic):              0.268\n",
      "Time:                            11:26:13   Log-Likelihood:                -2.2586\n",
      "No. Observations:                      43   AIC:                             8.517\n",
      "Df Residuals:                          41   BIC:                             12.04\n",
      "Df Model:                               1                                         \n",
      "Covariance Type:                nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept         0.6876      0.188      3.659      0.001       0.308       1.067\n",
      "ratio_mort_tb    -0.1288      0.115     -1.123      0.268      -0.361       0.103\n",
      "==============================================================================\n",
      "Omnibus:                        1.091   Durbin-Watson:                   2.164\n",
      "Prob(Omnibus):                  0.580   Jarque-Bera (JB):                0.803\n",
      "Skew:                           0.333   Prob(JB):                        0.669\n",
      "Kurtosis:                       2.929   Cond. No.                         10.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cities Over 100k + Smaller Cities + Grouped Cities\n",
    "\n",
    "# Table 6.6\n",
    "joinership_tb_mortality_4 = sm.formula.ols(\"total_joiners_per_1000 ~ ratio_mort_tb\", data = df_total).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TB MORTALITY AND CITIES OVER 100K + SMALLER CITIES + GROUPED CITIES:\")\n",
    "print(joinership_tb_mortality_4.summary())\n",
    "print(\"\\n\")\n",
    "\n",
    "# Figure 6.10\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_total.ratio_mort_tb, df_total.total_joiners_per_1000)\n",
    "plt.xlabel(\"TB Mortality Change from 1914 to 1918\")\n",
    "plt.ylabel(\"Total Number of Joiners per 1000\")\n",
    "plt.title(\"Joinership vs. TB Mortality Increase\")\n",
    "plt.plot([1.0, 2.2], [joinership_tb_mortality_4.params[0] + joinership_tb_mortality_4.params[1] * 1.0,\n",
    "                      joinership_tb_mortality_4.params[0] + joinership_tb_mortality_4.params[1] * 2.2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22eb030a-acfe-4749-b952-5d384d1d79c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# War Baby Joiners per 1000 vs. TB Mortality Increase 1914-1918"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de9a5d16-f634-4295-ada2-8b6e850ec3eb",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "BIVARIATE REGRESSION TABLE FOR TB MORTALITY AND CITIES OVER 100K + SMALLER CITIES + GROUPED CITIES:\n",
      "                                OLS Regression Results                               \n",
      "=====================================================================================\n",
      "Dep. Variable:     war_baby_joiners_per_1000   R-squared:                       0.026\n",
      "Model:                                   OLS   Adj. R-squared:                  0.002\n",
      "Method:                        Least Squares   F-statistic:                     1.076\n",
      "Date:                       Fri, 14 Oct 2022   Prob (F-statistic):              0.306\n",
      "Time:                               11:32:51   Log-Likelihood:                 53.718\n",
      "No. Observations:                         43   AIC:                            -103.4\n",
      "Df Residuals:                             41   BIC:                            -99.91\n",
      "Df Model:                                  1                                         \n",
      "Covariance Type:                   nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept         0.1453      0.051      2.842      0.007       0.042       0.249\n",
      "ratio_mort_tb    -0.0324      0.031     -1.037      0.306      -0.095       0.031\n",
      "==============================================================================\n",
      "Omnibus:                        2.060   Durbin-Watson:                   2.513\n",
      "Prob(Omnibus):                  0.357   Jarque-Bera (JB):                1.881\n",
      "Skew:                           0.415   Prob(JB):                        0.390\n",
      "Kurtosis:                       2.400   Cond. No.                         10.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cities Over 100k + Smaller Cities + Grouped Cities\n",
    "\n",
    "# Table 6.7\n",
    "war_baby_joinership_tb_mortality_4 = sm.formula.ols(\"war_baby_joiners_per_1000 ~ ratio_mort_tb\", data = df_total).fit()\n",
    "print(\"\\n\\nBIVARIATE REGRESSION TABLE FOR TB MORTALITY AND CITIES OVER 100K + SMALLER CITIES + GROUPED CITIES:\")\n",
    "print(war_baby_joinership_tb_mortality_4.summary())\n",
    "print(\"\\n\")\n",
    "\n",
    "# Figure 6.11\n",
    "plt.figure(figsize = (15, 10))\n",
    "plt.scatter(df_total.ratio_mort_tb, df_total.war_baby_joiners_per_1000)\n",
    "plt.xlabel(\"TB Mortality Change from 1914 to 1918\")\n",
    "plt.ylabel(\"Number of War Baby Joiners per 1000\")\n",
    "plt.title(\"War Baby Joinership vs. TB Mortality Increase\")\n",
    "plt.plot([1.0, 2.2], [war_baby_joinership_tb_mortality_4.params[0] + war_baby_joinership_tb_mortality_4.params[1] * 1.0,\n",
    "                      war_baby_joinership_tb_mortality_4.params[0] + war_baby_joinership_tb_mortality_4.params[1] * 2.2])\n",
    "plt.show()"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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